Validation of OPE is a significant but critical challenge for practical utility. The potential for human feedback is the least well explored especially for retrospective OPE. Technically, sensitivity analysis models are the main modeling frameworks that can allow the incorporation of domain knowledge that characterizes the severity of unobserved confounding in the system. For instance, it can help validate prospective as well as retrospective OPE tasks for complex settings such as the POMDP (Figure REF ). Currently, the most commonly used sensitivity model known as the marginal sensitivity model, which constrains deviations to the propensity of treatment in the presence of unobserved confounders, and has been widely explored for population and sub-population-level prospective OPE in reinforcement learning [1]}, [2]}. Model debugging of this kind has been explored for counterfactual OPE for discrete SCMs for managing sepsis among diabetic patients in [3]}.
<FIGURE>